Detect if GPU running on the device is compatible with TensorFlow GPUĭelegate, if not run the model using multiple CPU threads: Implementation 'org.tensorflow:tensorflow-lite-gpu:2.3.0' TFLite GPU delegate 2.3.0 or above is required. Check the module adle file that it contains the following Otherwise, TfLiteGpuDelegate Invoke: GpuDelegate must run on the same Note: The TensorFlow Lite Interpreter must be created on the same thread as when The use of delegates and the number of threads. ML Model Binding provides a way for developers to accelerate their code through The TFLite model under the ml directory in Android Studio. You can get back to this screen by double clicking Using the model, select Kotlin or Java, copy and paste the code under the The following screen will appear after the import is successful.

Optional: Select the second checkbox for importing TensorFlow GPU if you Note that the tooling willĬonfigure the module's dependency on your behalf with ML Model binding andĪll dependencies automatically inserted into your Android module's Right-click on the module you would like to use the TFLite model or click onįile, then New > Other > TensorFlow Lite Model Note: Required Android Studio 4.1 orĪbove Import a TensorFlow Lite model in Android Studio With typed objects such as Bitmap and Rect. Instead, developers can interact with the TensorFlow Lite model The wrapper code removes the need to interact directly withīyteBuffer. Settings for the project and generate wrapper classes based on the model Use Android Studio ML Model Bindingįor TensorFlow Lite models enhanced with metadata,ĭevelopers can use Android Studio ML Model Binding to automatically configure

Tooling, the TensorFlow Lite Codegen is also available. If you require more customisation or are using command line Graphical interface of Android Studio ML Model Binding is theĮasiest to use. Wrapper code to enable integration on Android. Using TensorFlow Lite Metadata, developers can generate

Is not backed by gradle android studio